{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bs9B64CpsOF0"
   },
   "source": [
    "#### 주의!!\n",
    "\n",
    "이 실습은 가급적 NVIDIA GPU가 설치된 컴퓨터 환경이거나 Google Colab에서 진행해주세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2NmYZYYhXrcZ"
   },
   "source": [
    "## 환경 준비 \n",
    "(Google Colab 환경에서 사용하세요)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6-bFpckCXrcb"
   },
   "outputs": [],
   "source": [
    "!pip install transformers\n",
    "!pip install accelerate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cvFHjoTCXrcc"
   },
   "source": [
    "## 데이터 다운로드\n",
    "(Google Colab 환경에서 사용하세요)\n",
    "\n",
    "\n",
    "데이터는 재량으로 huggingface hub를 이용하여 받으셔도 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HbKNloVoXrcd"
   },
   "outputs": [],
   "source": [
    "!mkdir -p data_in/KOR/naver_movie\n",
    "!wget https://raw.githubusercontent.com/NLP-kr/tensorflow-ml-nlp-tf2/master/7.PRETRAIN_METHOD/data_in/KOR/naver_movie/ratings_train.txt \\\n",
    "              -O data_in/KOR/naver_movie/ratings_train.txt\n",
    "!wget https://raw.githubusercontent.com/NLP-kr/tensorflow-ml-nlp-tf2/master/7.PRETRAIN_METHOD/data_in/KOR/naver_movie/ratings_test.txt \\\n",
    "              -O data_in/KOR/naver_movie/ratings_test.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xs88fDX8Xrcd",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "import torch\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "\n",
    "import random\n",
    "from random import sample\n",
    "\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "s6dM4ebxXrcg"
   },
   "outputs": [],
   "source": [
    "SEED_NUM = 1234\n",
    "np.random.seed(SEED_NUM)\n",
    "random.seed(SEED_NUM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alpaca-polyglot nsmc 테스트"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alpaca-polyglot 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
      "df5c9e1d628c44749a46c1ed975fa968",
      "4bb799ac88284a0587b69618b4bb322f",
      "47f85f5a79964908b3abf3e66d363006",
      "673c11eb2cf844f694a5e95436c01c18",
      "6a14694496b446bdbe73dee220b41afe",
      "3eed58da360044469c7028e0ec8828ce",
      "743e24b68d5944af94edb4aa05c80922",
      "f7be3d1ee3294854a8aee0f4403b22b2",
      "7339481a49c141d1bd6120b75e37ba57",
      "490291bd69cd452cb4421a6ccb8d30ad",
      "54bbae66a3e64667b2abee10808d9974",
      "0af4bdebff084bc6b474aec3cdcb34ac",
      "cb47332dd9d54be7a74fa4aff50b86db",
      "4cd1de98da384ba1b448051c6997e5cd",
      "93b6fe0a78524fceae104603fbf3c435",
      "df9bfda580254697a4cb95c9f03b2cc1",
      "c116ce758d894c4795b65f9a22f1fd28",
      "57ce1bcdae624bf290f5000854b39ffb",
      "4fbbce68f80049ab80a0e068abddc156",
      "e7565d3ad0d04b54a4f0d1d458ac962a",
      "49bb5be84f874b89a95dac6bd87db2a6",
      "d70853c679e94c75b1e28f82a2791d9d",
      "3284f190a64a4636991d7e5c6226103e",
      "b3c0a311c653400b970ea23764b13489",
      "4076e6e04dd94cb9873d4fae6808c7b2",
      "4189a2ee74cd4ecba20ebada68fe1487",
      "146427ea14ac4b09a47b8de8d4325f31",
      "58c07d0efada453c9daace48e77f9358",
      "62c55f2a516c4bb3aee759d371196385",
      "3d9a811474ab43fcbe825711ee8b96f0",
      "e786b4f96e874452982c92e40b2f19be",
      "f7cd635ca6aa4c79bb9d513c7c561b5f",
      "aa9e526c5965437095cbd1a8a54406ff"
     ]
    },
    "id": "lAaKKUqbXrch",
    "outputId": "71810ac7-1b9f-479f-8527-244eae69104d"
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/polyglot-ko-5.8b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "90209f66d19e44ed9e2d9284eb9d0edb",
      "e80dd435cb8b4102857263a50083dad1",
      "bc74439b1c5446638833df7608aa320a",
      "ff51642a145445b8aa8c073ef43cc595",
      "b9b88db2460749fb956b71d86cde351d",
      "6cfd17b7a0a54fa99fe54ca0bdbb0be2",
      "355b52c43569480bbb75757bec948a26",
      "8703e77ab90744378ffc1e2addcace73",
      "f0d02190652444f78c321632362a29e3",
      "4ae19c4cfd354caaa70415097aba3437",
      "07318ac2fe9b4c7bb62607f509c7164a",
      "f69899ba95df4797a702c981c833c41f",
      "f42ec0b3e4db4602ae33cb4d276b8115",
      "f2f176ed89964a83858ebe42e62b6023",
      "ce3a0609950e4934b5049c2504437c33",
      "94b1bd35bf6244ebac3eeb3279960a99",
      "587e38ee698242dd9606c5feb60141a6",
      "b729fc5096b84f779b2fbb2b65853e9f",
      "67f908b9beaf4065a25580058bcd6c10",
      "5624e71490c7463d987365cbc9d9fd25",
      "0c97093f31ec4e2d86695cd41e4adae6",
      "08d01c9dbfcb4e84a76e1fbb4c93b983"
     ]
    },
    "id": "9J5VOzCwXrcj",
    "outputId": "040054bf-b980-4777-b03e-14c9245ab06f",
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "cls_model = AutoModelForCausalLM.from_pretrained(\"beomi/KoAlpaca-Polyglot\",\n",
    "                                                 torch_dtype=torch.float16,\n",
    "                                                 low_cpu_mem_usage=True).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tnMMmMdoFkxb"
   },
   "outputs": [],
   "source": [
    "cls_model.config.max_length = 2048\n",
    "cls_model.config.pad_token_id = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pCN8Lh7gXrch"
   },
   "source": [
    "### 퓨샷 러닝을 위한 네이버 영화 리뷰 데이터 EDA 및 구성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Ct1IbwATXrci"
   },
   "outputs": [],
   "source": [
    "# 데이터 전처리 준비\n",
    "DATA_IN_PATH = './data_in/KOR'\n",
    "DATA_OUT_PATH = './data_out/KOR'\n",
    "\n",
    "DATA_TRAIN_PATH = os.path.join(DATA_IN_PATH, 'naver_movie', 'ratings_train.txt')\n",
    "DATA_TEST_PATH = os.path.join(DATA_IN_PATH, 'naver_movie', 'ratings_test.txt')\n",
    "\n",
    "train_data = pd.read_csv(DATA_TRAIN_PATH, header = 0, delimiter = '\\t', quoting = 3)\n",
    "train_data = train_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WED9P9SUSyR9"
   },
   "outputs": [],
   "source": [
    "print('데이터 positive 라벨: ', '긍정')\n",
    "print('데이터 negative 라벨: ', '부정')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WaQ1miXfwQfn"
   },
   "outputs": [],
   "source": [
    "print('학습 예시 케이스 구조: ', '문장: 오늘 기분이 좋아\\n감정: 긍정\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7h0USc0RxQqG"
   },
   "outputs": [],
   "source": [
    "print('gpt 최대 토큰 길이: ', cls_model.config.max_position_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MRwI-RcOyFRj"
   },
   "outputs": [],
   "source": [
    "sent_lens = [len(tokenizer(s).input_ids) for s in tqdm(train_data['document'])]\n",
    "\n",
    "print('Few shot 케이스 토큰 평균 길이: ', np.mean(sent_lens))\n",
    "print('Few shot 케이스 토큰 최대 길이: ', np.max(sent_lens))\n",
    "print('Few shot 케이스 토큰 길이 표준편차: ',np.std(sent_lens))\n",
    "print('Few shot 케이스 토큰 길이 80 퍼센타일: ',np.percentile(sent_lens, 80))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PdIWfc6Pzyfz"
   },
   "outputs": [],
   "source": [
    "train_fewshot_data = []\n",
    "\n",
    "for train_sent, train_label in tqdm(train_data[['document', 'label']].values):\n",
    "    tokens = tokenizer(train_sent).input_ids\n",
    "\n",
    "    if len(tokens) <= 25:\n",
    "        train_fewshot_data.append((train_sent, train_label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4jFe7XMeXrcl"
   },
   "source": [
    "### 네이버 영화 리뷰 데이터를 활용한 퓨샷 러닝 및 평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1_OhF3hVhK0y"
   },
   "outputs": [],
   "source": [
    "test_data = pd.read_csv(DATA_TEST_PATH, header=0, delimiter='\\t', quoting=3)\n",
    "test_data = test_data.dropna()\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "liE91_rhsQdY"
   },
   "outputs": [],
   "source": [
    "# Full Dataset\n",
    "# sample_size = len(test_data)\n",
    "\n",
    "# Sampled Dataset\n",
    "sample_size = 500\n",
    "\n",
    "train_fewshot_samples = []\n",
    "\n",
    "for _ in range(sample_size):\n",
    "    fewshot_examples = sample(train_fewshot_data, 10)\n",
    "    train_fewshot_samples.append(fewshot_examples)\n",
    "\n",
    "if sample_size < len(test_data['id']):\n",
    "    test_data = test_data.sample(sample_size, random_state=SEED_NUM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### koalpaca 프롬프트 1 방식 실험"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "s-ZiFs-aRIXy"
   },
   "outputs": [],
   "source": [
    "def build_prompt_text(sent):\n",
    "    return \"문장: \" + sent + '\\n감정:'\n",
    "\n",
    "def clean_text(sent):\n",
    "    sent_clean = re.sub(\"[^가-힣ㄱ-ㅎㅏ-ㅣ\\\\s]\", \"\", sent)\n",
    "    return sent_clean\n",
    "\n",
    "real_labels = []\n",
    "pred_tokens = []\n",
    "\n",
    "total_len = len(test_data[['document','label']].values)\n",
    "\n",
    "for i, (test_sent, test_label) in tqdm(enumerate(test_data[['document','label']].values), total=total_len):\n",
    "    prompt_text = ''\n",
    "\n",
    "    for ex in train_fewshot_samples[i]:\n",
    "        example_text, example_label = ex\n",
    "        cleaned_example_text = clean_text(example_text)\n",
    "        appended_prompt_example_text = build_prompt_text(cleaned_example_text)\n",
    "        appended_prompt_example_text += ' 긍정\\n' if example_label == 1 else ' 부정\\n'\n",
    "        prompt_text += appended_prompt_example_text\n",
    "\n",
    "    cleaned_sent = clean_text(test_sent)\n",
    "    appended_prompt_sent = build_prompt_text(cleaned_sent)\n",
    "\n",
    "    prompt_text += appended_prompt_sent\n",
    "\n",
    "    tokens = tokenizer(prompt_text, return_tensors=\"pt\")\n",
    "    token_ids, attn_mask = tokens.input_ids.cuda(), tokens.attention_mask.cuda()\n",
    "    gen_tokens = cls_model.generate(input_ids=token_ids, attention_mask=attn_mask,\n",
    "                                    max_new_tokens=1, pad_token_id=0)\n",
    "    pred = tokenizer.batch_decode(gen_tokens[:, -1])[0].strip()\n",
    "\n",
    "    pred_tokens.append(pred)\n",
    "    real_labels.append('긍정' if test_label == 1 else '부정')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0oZ1GfUPeuec"
   },
   "outputs": [],
   "source": [
    "accuracy_match = [p == t for p, t in zip(pred_tokens, real_labels)]\n",
    "accuracy = len([m for m in accuracy_match if m]) / len(real_labels)\n",
    "\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### koalpaca 프롬프트 2 방식 실험"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vMYgRRGI-Gu4"
   },
   "outputs": [],
   "source": [
    "def build_prompt_text(sent):\n",
    "    return '다음 문장은 긍정일까요 부정일까요?\\n' + sent + '\\n정답:'\n",
    "\n",
    "real_labels = []\n",
    "pred_tokens = []\n",
    "\n",
    "\n",
    "real_labels = []\n",
    "pred_tokens = []\n",
    "\n",
    "total_len = len(test_data[['document','label']].values)\n",
    "\n",
    "for i, (test_sent, test_label) in tqdm(enumerate(test_data[['document','label']].values), total=total_len):\n",
    "    prompt_text = ''\n",
    "\n",
    "    for ex in train_fewshot_samples[i]:\n",
    "        example_text, example_label = ex\n",
    "        cleaned_example_text = clean_text(example_text)\n",
    "        appended_prompt_example_text = build_prompt_text(cleaned_example_text)\n",
    "        appended_prompt_example_text += ' 긍정\\n' if example_label == 1 else ' 부정\\n'\n",
    "        prompt_text += appended_prompt_example_text\n",
    "\n",
    "    cleaned_sent = clean_text(test_sent)\n",
    "    appended_prompt_sent = build_prompt_text(cleaned_sent)\n",
    "\n",
    "    prompt_text += appended_prompt_sent\n",
    "\n",
    "    tokens = tokenizer(prompt_text, return_tensors=\"pt\")\n",
    "    token_ids, attn_mask = tokens.input_ids.cuda(), tokens.attention_mask.cuda()\n",
    "    gen_tokens = cls_model.generate(input_ids=token_ids, attention_mask=attn_mask,\n",
    "                                    max_new_tokens=1, pad_token_id=0)\n",
    "    pred = tokenizer.batch_decode(gen_tokens[:, -1])[0].strip()\n",
    "\n",
    "    pred_tokens.append(pred)\n",
    "    real_labels.append('긍정' if test_label == 1 else '부정')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8ufjRihAzNBK"
   },
   "outputs": [],
   "source": [
    "accuracy_match = [p == t for p, t in zip(pred_tokens, real_labels)]\n",
    "accuracy = len([m for m in accuracy_match if m]) / len(real_labels)\n",
    "\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## polyglot 5.8b nsmc 테스트"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### polyglot 로드\n",
    "\n",
    "colab에서는 유료버전만 실행 가능합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/polyglot-ko-5.8b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5w-ddZOSCIYv"
   },
   "outputs": [],
   "source": [
    "cls_model = AutoModelForCausalLM.from_pretrained(\"EleutherAI/polyglot-ko-5.8b\",\n",
    "                                                 torch_dtype=torch.float16,\n",
    "                                                 low_cpu_mem_usage=True).cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### polyglot 프롬프트 1 방식 실험"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "s-ZiFs-aRIXy"
   },
   "outputs": [],
   "source": [
    "def build_prompt_text(sent):\n",
    "    return \"문장: \" + sent + '\\n감정:'\n",
    "\n",
    "def clean_text(sent):\n",
    "    sent_clean = re.sub(\"[^가-힣ㄱ-ㅎㅏ-ㅣ\\\\s]\", \"\", sent)\n",
    "    return sent_clean\n",
    "\n",
    "real_labels = []\n",
    "pred_tokens = []\n",
    "\n",
    "total_len = len(test_data[['document','label']].values)\n",
    "\n",
    "for i, (test_sent, test_label) in tqdm(enumerate(test_data[['document','label']].values), total=total_len):\n",
    "    prompt_text = ''\n",
    "\n",
    "    for ex in train_fewshot_samples[i]:\n",
    "        example_text, example_label = ex\n",
    "        cleaned_example_text = clean_text(example_text)\n",
    "        appended_prompt_example_text = build_prompt_text(cleaned_example_text)\n",
    "        appended_prompt_example_text += ' 긍정\\n' if example_label == 1 else ' 부정\\n'\n",
    "        prompt_text += appended_prompt_example_text\n",
    "\n",
    "    cleaned_sent = clean_text(test_sent)\n",
    "    appended_prompt_sent = build_prompt_text(cleaned_sent)\n",
    "\n",
    "    prompt_text += appended_prompt_sent\n",
    "\n",
    "    tokens = tokenizer(prompt_text, return_tensors=\"pt\")\n",
    "    token_ids, attn_mask = tokens.input_ids.cuda(), tokens.attention_mask.cuda()\n",
    "    gen_tokens = cls_model.generate(input_ids=token_ids, attention_mask=attn_mask,\n",
    "                                    max_new_tokens=1, pad_token_id=0)\n",
    "    pred = tokenizer.batch_decode(gen_tokens[:, -1])[0].strip()\n",
    "\n",
    "    pred_tokens.append(pred)\n",
    "    real_labels.append('긍정' if test_label == 1 else '부정')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0oZ1GfUPeuec"
   },
   "outputs": [],
   "source": [
    "accuracy_match = [p == t for p, t in zip(pred_tokens, real_labels)]\n",
    "accuracy = len([m for m in accuracy_match if m]) / len(real_labels)\n",
    "\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### polyglot 프롬프트 2 방식 실험"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "def build_prompt_text(sent):\n",
    "    return '다음 문장은 긍정일까요 부정일까요?\\n' + sent + '\\n정답:'\n",
    "\n",
    "real_labels = []\n",
    "pred_tokens = []\n",
    "\n",
    "\n",
    "real_labels = []\n",
    "pred_tokens = []\n",
    "\n",
    "total_len = len(test_data[['document','label']].values)\n",
    "\n",
    "for i, (test_sent, test_label) in tqdm(enumerate(test_data[['document','label']].values), total=total_len):\n",
    "    prompt_text = ''\n",
    "\n",
    "    for ex in train_fewshot_samples[i]:\n",
    "        example_text, example_label = ex\n",
    "        cleaned_example_text = clean_text(example_text)\n",
    "        appended_prompt_example_text = build_prompt_text(cleaned_example_text)\n",
    "        appended_prompt_example_text += ' 긍정\\n' if example_label == 1 else ' 부정\\n'\n",
    "        prompt_text += appended_prompt_example_text\n",
    "\n",
    "    cleaned_sent = clean_text(test_sent)\n",
    "    appended_prompt_sent = build_prompt_text(cleaned_sent)\n",
    "\n",
    "    prompt_text += appended_prompt_sent\n",
    "\n",
    "    tokens = tokenizer(prompt_text, return_tensors=\"pt\")\n",
    "    token_ids, attn_mask = tokens.input_ids.cuda(), tokens.attention_mask.cuda()\n",
    "    gen_tokens = cls_model.generate(input_ids=token_ids, attention_mask=attn_mask,\n",
    "                                    max_new_tokens=1, pad_token_id=0)\n",
    "    pred = tokenizer.batch_decode(gen_tokens[:, -1])[0].strip()\n",
    "\n",
    "    pred_tokens.append(pred)\n",
    "    real_labels.append('긍정' if test_label == 1 else '부정')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8ufjRihAzNBK"
   },
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    "accuracy_match = [p == t for p, t in zip(pred_tokens, real_labels)]\n",
    "accuracy = len([m for m in accuracy_match if m]) / len(real_labels)\n",
    "\n",
    "print(accuracy)"
   ]
  }
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